You maintain a Claude application that uses Claude Sonnet 4.5 across several production workflows.
Anthropic released Claude Sonnet 4.7, which your evaluation suite shows performing 8% better on your highest-volume task. However, this version produces different output formatting on two of your structured- extraction prompts that downstream consumers parse with regex-based code.
To roll out the upgrade, you would...

• A. Rewrite the downstream parsing code to accept a wider range of output formats to ensure the application is resilient to future model upgrades without requiring prompt changes.
• B. Keep the older model in production to preserve the fragile parsing layer that may risk breaking with any model change.
• C. Switch the model identifier in the application's configuration to the new model, then monitor production for parsing failures to be fixed as failures surface.
• D. Adjust the prompts to constrain output format, re-run evaluations against the expected parsing-layer schema, then roll the model out with a feature flag and the option to revert per workflow.

The Anthropic API deprecated a request parameter that your Claude application uses in approximately 40 places across the codebase. The deprecation notice gives a six-month window before the parameter is removed and recommends a replacement parameter with slightly different semantics.
You would respond to the deprecation by...

• A. Swapping all 40 call sites in a single change right away to prevent drawn-out migration work that will delay ongoing functioning.
• B. Migrating all 40 call sites in a single change near the removal date to ensure the deprecated parameter continues to work as long as possible.
• C. Keeping the deprecated parameter in place while writing a wrapper function around it to insulate the rest of the codebase from the eventual change.
• D. Adding regression tests to cover the parameter's behavior, then migrating call sites in batches that you validate against regression tests.

Your Claude application is deployed to development, staging, and production environments. Each environment uses a different model version, different prompt versions, and different plugin dependencies, but the configuration is currently scattered across environment variables, hardcoded values, and undocumented setup scripts.
How would you manage the configuration?

• A. Standardize all environments to use the same configuration values to eliminate the differences between development, staging, and production.
• B. Use the latest available model version everywhere and stop pinning model versions, on the grounds that pinning adds maintenance overhead the team should aim to reduce.
• C. Consolidate the configuration into a version-controlled system documenting model version pinning, prompt versioning, and plugin dependencies for each environment.
• D. Move all configuration into hardcoded application code to reduce reliance on external configuration sources that are difficult to track over time.

The Claude application your team built has grown over six months, and the prompt-handling code has accumulated duplication and tangled control flow. The functionality is working, but new features are getting harder to add.
How would you address this?

• A. Refactor the prompt-handling code in small increments as part of each new feature ticket, treating the cleanup as a side effect of feature work.
• B. Continue adding features and plan a refactoring pass after the next two release cycles when the team has more bandwidth for internal work.
• C. Move all the prompt-handling code into a single large function to reduce the number of files developers have to navigate when reading the code.
• D. Plan a refactoring pass to consolidate duplicated logic, separate concerns, and simplify control flow before adding new features.